Gaussian.cpp

Go to the documentation of this file.

/*
 * This program is free software; you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation; either version 3 of the License, or
 * (at your option) any later version.
 *
 * Written (W) 2011 Alesis Novik
 * Copyright (C) 2011 Berlin Institute of Technology and Max-Planck-Society
 */
#include <shogun/lib/config.h>

#ifdef HAVE_LAPACK

#include <shogun/distributions/Gaussian.h>
#include <shogun/mathematics/Math.h>
#include <shogun/base/Parameter.h>

using namespace shogun;

CGaussian::CGaussian() : CDistribution(), m_constant(0), m_d(), m_u(), m_mean(), m_cov_type(FULL)
{
    register_params();
}

CGaussian::CGaussian(const SGVector<float64_t> mean, SGMatrix<float64_t> cov,
                    ECovType cov_type) : CDistribution(), m_d(), m_u(), m_cov_type(cov_type)
{
    ASSERT(mean.vlen==cov.num_rows);
    ASSERT(cov.num_rows==cov.num_cols);

    m_mean=mean;

    if (cov.num_rows==1)
        m_cov_type=SPHERICAL;

    decompose_cov(cov);
    init();
    register_params();
}

void CGaussian::init()
{
    m_constant=CMath::log(2*M_PI)*m_mean.vlen;
    switch (m_cov_type)
    {
        case FULL:
        case DIAG:
            for (int32_t i=0; i<m_d.vlen; i++)
                m_constant+=CMath::log(m_d.vector[i]);
            break;
        case SPHERICAL:
            m_constant+=m_mean.vlen*CMath::log(m_d.vector[0]);
            break;
    }
}

CGaussian::~CGaussian()
{
}

bool CGaussian::train(CFeatures* data)
{
    // init features with data if necessary and assure type is correct
    if (data)
    {
        if (!data->has_property(FP_DOT))
                SG_ERROR("Specified features are not of type CDotFeatures\n");
        set_features(data);
    }

    CDotFeatures* dotdata=(CDotFeatures *) data;
    m_mean=dotdata->get_mean();
    SGMatrix<float64_t> cov=dotdata->get_cov();
    decompose_cov(cov);
    init();
    return true;
}

int32_t CGaussian::get_num_model_parameters()
{
    switch (m_cov_type)
    {
        case FULL:
            return m_u.num_rows*m_u.num_cols+m_d.vlen+m_mean.vlen;
        case DIAG:
            return m_d.vlen+m_mean.vlen;
        case SPHERICAL:
            return 1+m_mean.vlen;
    }
    return 0;
}

float64_t CGaussian::get_log_model_parameter(int32_t num_param)
{
    SG_NOTIMPLEMENTED;
    return 0;
}

float64_t CGaussian::get_log_derivative(int32_t num_param, int32_t num_example)
{
    SG_NOTIMPLEMENTED;
    return 0;
}

float64_t CGaussian::get_log_likelihood_example(int32_t num_example)
{
    ASSERT(features->has_property(FP_DOT));
    SGVector<float64_t> v=((CDotFeatures *)features)->get_computed_dot_feature_vector(num_example);
    float64_t answer=compute_log_PDF(v);
    return answer;
}

float64_t CGaussian::compute_log_PDF(SGVector<float64_t> point)
{
    ASSERT(m_mean.vector && m_d.vector);
    ASSERT(point.vlen == m_mean.vlen);
    float64_t* difference=SG_MALLOC(float64_t, m_mean.vlen);
    memcpy(difference, point.vector, sizeof(float64_t)*m_mean.vlen);

    for (int32_t i = 0; i < m_mean.vlen; i++)
        difference[i] -= m_mean.vector[i];

    float64_t answer=m_constant;

    if (m_cov_type==FULL)
    {
        float64_t* temp_holder=SG_MALLOC(float64_t, m_d.vlen);
        cblas_dgemv(CblasRowMajor, CblasNoTrans, m_d.vlen, m_d.vlen,
                    1, m_u.matrix, m_d.vlen, difference, 1, 0, temp_holder, 1);

        for (int32_t i=0; i<m_d.vlen; i++)
            answer+=temp_holder[i]*temp_holder[i]/m_d.vector[i];

        SG_FREE(temp_holder);
    }
    else if (m_cov_type==DIAG)
    {
        for (int32_t i=0; i<m_mean.vlen; i++)
            answer+=difference[i]*difference[i]/m_d.vector[i];
    }
    else
    {
        for (int32_t i=0; i<m_mean.vlen; i++)
            answer+=difference[i]*difference[i]/m_d.vector[0];
    }

    SG_FREE(difference);

    return -0.5*answer;
}

SGVector<float64_t> CGaussian::get_mean()
{
    return m_mean;
}

void CGaussian::set_mean(SGVector<float64_t> mean)
{
    if (mean.vlen==1)
        m_cov_type=SPHERICAL;

    m_mean=mean;
}

void CGaussian::set_cov(SGMatrix<float64_t> cov)
{
    ASSERT(cov.num_rows==cov.num_cols);
    ASSERT(cov.num_rows==m_mean.vlen);
    decompose_cov(cov);
    init();
}

void CGaussian::set_d(const SGVector<float64_t> d)
{
    m_d = d;
    init();
}

SGMatrix<float64_t> CGaussian::get_cov()
{
    float64_t* cov=SG_MALLOC(float64_t, m_mean.vlen*m_mean.vlen);
    memset(cov, 0, sizeof(float64_t)*m_mean.vlen*m_mean.vlen);

    if (m_cov_type==FULL)
    {
        if (!m_u.matrix)
            SG_ERROR("Unitary matrix not set\n");

        float64_t* temp_holder=SG_MALLOC(float64_t, m_d.vlen*m_d.vlen);
        float64_t* diag_holder=SG_MALLOC(float64_t, m_d.vlen*m_d.vlen);
        memset(diag_holder, 0, sizeof(float64_t)*m_d.vlen*m_d.vlen);
        for(int32_t i=0; i<m_d.vlen; i++)
            diag_holder[i*m_d.vlen+i]=m_d.vector[i];

        cblas_dgemm(CblasRowMajor, CblasTrans, CblasNoTrans,
                    m_d.vlen, m_d.vlen, m_d.vlen, 1, m_u.matrix, m_d.vlen,
                    diag_holder, m_d.vlen, 0, temp_holder, m_d.vlen);
        cblas_dgemm(CblasRowMajor, CblasNoTrans, CblasNoTrans,
                    m_d.vlen, m_d.vlen, m_d.vlen, 1, temp_holder, m_d.vlen,
                    m_u.matrix, m_d.vlen, 0, cov, m_d.vlen);

        SG_FREE(diag_holder);
        SG_FREE(temp_holder);
    }
    else if (m_cov_type==DIAG)
    {
        for (int32_t i=0; i<m_d.vlen; i++)
            cov[i*m_d.vlen+i]=m_d.vector[i];
    }
    else
    {
        for (int32_t i=0; i<m_mean.vlen; i++)
            cov[i*m_mean.vlen+i]=m_d.vector[0];
    }
    return SGMatrix<float64_t>(cov, m_mean.vlen, m_mean.vlen, false);//fix needed
}

void CGaussian::register_params()
{
    m_parameters->add(&m_u, "m_u", "Unitary matrix.");
    m_parameters->add(&m_d, "m_d", "Diagonal.");
    m_parameters->add(&m_mean, "m_mean", "Mean.");
    m_parameters->add(&m_constant, "m_constant", "Constant part.");
    m_parameters->add((machine_int_t*)&m_cov_type, "m_cov_type", "Covariance type.");
}

void CGaussian::decompose_cov(SGMatrix<float64_t> cov)
{
    switch (m_cov_type)
    {
        case FULL:
            m_u=SGMatrix<float64_t>(cov.num_rows,cov.num_rows);
            memcpy(m_u.matrix, cov.matrix, sizeof(float64_t)*cov.num_rows*cov.num_rows);

            m_d.vector=SGMatrix<float64_t>::compute_eigenvectors(m_u.matrix, cov.num_rows, cov.num_rows);
            m_d.vlen=cov.num_rows;
            m_u.num_rows=cov.num_rows;
            m_u.num_cols=cov.num_rows;
            break;
        case DIAG:
            m_d.vector=SG_MALLOC(float64_t, cov.num_rows);

            for (int32_t i=0; i<cov.num_rows; i++)
                m_d.vector[i]=cov.matrix[i*cov.num_rows+i];

            m_d.vlen=cov.num_rows;
            break;
        case SPHERICAL:
            m_d.vector=SG_MALLOC(float64_t, 1);

            m_d.vector[0]=cov.matrix[0];
            m_d.vlen=1;
            break;
    }
}

SGVector<float64_t> CGaussian::sample()
{
    float64_t* r_matrix=SG_MALLOC(float64_t, m_mean.vlen*m_mean.vlen);
    memset(r_matrix, 0, m_mean.vlen*m_mean.vlen*sizeof(float64_t));

    switch (m_cov_type)
    {
        case FULL:
        case DIAG:
            for (int32_t i=0; i<m_mean.vlen; i++)
                r_matrix[i*m_mean.vlen+i]=CMath::sqrt(m_d.vector[i]);

            break;
        case SPHERICAL:
            for (int32_t i=0; i<m_mean.vlen; i++)
                r_matrix[i*m_mean.vlen+i]=CMath::sqrt(m_d.vector[0]);

            break;
    }

    float64_t* random_vec=SG_MALLOC(float64_t, m_mean.vlen);

    for (int32_t i=0; i<m_mean.vlen; i++)
        random_vec[i]=CMath::randn_double();

    if (m_cov_type==FULL)
    {
        float64_t* temp_matrix=SG_MALLOC(float64_t, m_d.vlen*m_d.vlen);
        cblas_dgemm(CblasRowMajor, CblasNoTrans, CblasNoTrans,
                    m_d.vlen, m_d.vlen, m_d.vlen, 1, m_u.matrix, m_d.vlen,
                    r_matrix, m_d.vlen, 0, temp_matrix, m_d.vlen);
        SG_FREE(r_matrix);
        r_matrix=temp_matrix;
    }

    float64_t* samp=SG_MALLOC(float64_t, m_mean.vlen);

    cblas_dgemv(CblasRowMajor, CblasNoTrans, m_mean.vlen, m_mean.vlen,
                1, r_matrix, m_mean.vlen, random_vec, 1, 0, samp, 1);

    for (int32_t i=0; i<m_mean.vlen; i++)
        samp[i]+=m_mean.vector[i];

    SG_FREE(random_vec);
    SG_FREE(r_matrix);

    return SGVector<float64_t>(samp, m_mean.vlen, false);//fix needed
}

#endif